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Related Concept Videos

Microsoft Excel: Pearson's Correlation01:18

Microsoft Excel: Pearson's Correlation

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Microsoft Excel is a powerful tool for statistical analysis, including calculating Pearson's correlation coefficient, which measures the strength and direction of a linear relationship between two continuous variables. Pearson's correlation coefficient, often denoted as "r," ranges from -1 to 1. A value close to 1 indicates a strong positive correlation, meaning as one variable increases, the other does too. A value close to -1 indicates a strong negative correlation, implying...
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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Correlation and Regression00:53

Correlation and Regression

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In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
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Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
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Coefficient of Correlation01:12

Coefficient of Correlation

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The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
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Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

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The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
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COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach.

Dilber Uzun Ozsahin1,2, Efe Precious Onakpojeruo2, Basil Bartholomew Duwa2

  • 1Department of Medical Diagnostic Imaging, College of Health Science, University of Sharjah, Sharjah 27272, United Arab Emirates.

Diagnostics (Basel, Switzerland)
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Summary

This study compared COVID-19 cases and mortality between Israel and Greece, finding multiple linear regression (MLR) more accurate than artificial neural networks (ANN) for predicting cases. MLR achieved 98% accuracy in forecasting COVID-19 spread.

Keywords:
ANNCOVID-19IsraelMLRcoronavirus

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Area of Science:

  • Epidemiology and Public Health
  • Artificial Intelligence in Healthcare
  • Biostatistics

Background:

  • The COVID-19 pandemic, caused by SARS-CoV-2, has had a profound global impact, necessitating effective control and prediction strategies.
  • Understanding transmission dynamics and developing accurate predictive models are crucial for mitigating the spread of infectious diseases like COVID-19.
  • International comparisons of disease burden and the influence of factors like vaccination rates are vital for informed public health policy.

Purpose of the Study:

  • To compare weekly and monthly COVID-19 cases and mortality between Israel and Greece.
  • To evaluate the impact of vaccination rates on COVID-19 mortality in Israel.
  • To predict future COVID-19 cases in Israel and Greece using artificial intelligence models.

Main Methods:

  • Comparative analysis of COVID-19 epidemiological data (cases and mortality) between Israel and Greece.
  • Correlation analysis to assess the influence of vaccination rates on COVID-19 mortality.
  • Development and evaluation of artificial intelligence models, specifically Artificial Neural Network (ANN) and Multiple Linear Regression (MLR), for case prediction.
  • Model performance was assessed using determination coefficient (R2), Mean Square Error (MSE), Root Mean Square Error (RMSE), and correlation coefficient (R).

Main Results:

  • Multiple Linear Regression (MLR) demonstrated superior performance over Artificial Neural Network (ANN) in predicting COVID-19 cases, indicated by a higher determination coefficient (R2).
  • The MLR model achieved an accuracy of 98% in predicting COVID-19 cases, while the ANN model achieved 94% accuracy.
  • The study identified statistical advantages of MLR over ANN, particularly in modeling linear patterns observed in the data.

Conclusions:

  • Multiple Linear Regression is a highly accurate and efficient model for predicting COVID-19 cases, outperforming Artificial Neural Networks in this study.
  • The findings support the use of MLR for epidemiological forecasting, aiding in the deployment of effective mitigation strategies to minimize virus spread.
  • Comparative analysis and predictive modeling are essential tools for managing and controlling pandemics like COVID-19.